Using Script-Fu in the GNU Image Manipulation Program to Automate “Smart” Sharpening

The GNU Image Manipulation Program, commonly abbreviated as “the Gimp,” is a free, open-source image editing program. Many photo restoration techniques exist to enhance images by making them clearer. This project focused on automating a powerful, yet complicated, method called “smart” sharpening. To accomplish this, a script was written in the Gimp’s Script-Fu language. After completion of the script, “smart” sharpening was compared to similar image sharpening procedures to evaluate its effectiveness. Despite surprising results on some types of images, more research is needed to further improve the script. Introduction The GNU Image Manipulation Program, commonly abbreviated as “the Gimp,” is a free, open-source image editing program similar to Adobe Photoshop. A default installation of the Gimp includes filters for common photo restoration tasks, including edge detection, matrix convolution, Gaussian blurs, and sharpening. The Gimp also has support for channels and layers, allowing for advanced blending and delicate drawing on images. Digital cameras have progressively become more advanced, but many still blur photographs during capture and compression. To repair this damage, several algorithms exist to sharpen images. The simplest of these, called convolution, uses a matrix to replace the value of each pixel. The center of the matrix represents the modified point, and surrounding pixels are multiplied by their corresponding factors in the matrix. These products are added together to create the replacement value for the center pixel. Another method to sharpen pictures, called unsharp masking, combines the original image and a smoothed version to enhance edges [1]. “Smart” sharpening is a process that sharpens the image without increasing image noise. Instead of applying a uniform filter across the image, it focuses on the edges of the image and avoids changing its colors [2]. First, the procedure duplicates the image and extracts the Value channel from the HSV (Hue Saturation Value) decomposition of brightness levels. A sharpening layer is created on the original image by copying the Value channel. An edge detection algorithm is passed over the duplicate image, which is further filtered by modifying light levels and blurring the result. This becomes the edge mask that is added to the sharpening layer. An unsharp mask on this sharpening layer creates the final product, a clearer image than the original. Most photo restoration techniques can be automated by scripting in the Gimp’s Script-Fu programming language. The SOID (Scheme in One Defun) interpreter for Script-Fu allows for cross-platform development of such tools by providing a simple interface to the Gimp’s functions [3]. Automation of common tasks reduces repetition, saves time, decreases the chance of human error, and simplifies processes by combining steps into one central command. This project will focus on the development of a script to automate the “smart” sharpening algorithm. After the program is completed, the “smart” sharpening technique will be compared to similar methods of photo sharpening to compare their effectiveness on enhancing images. Methods Online tutorials were helpful in gathering background knowledge about photo restoration techniques. The “smart” sharpening technique created by Eric Jeschke was chosen for this project because of its complexity and remarkable results [2]. The Gimp was chosen for image processing because of its cross-platform compatibility, functionality, and support for scripting. At first, the “smart” sharpening technique was going to be programmed in Perl using the Gimp::Fu module. However, limitations against using Perl in Windows XP encouraged development in Scheme. Only Linux builds of the Perl module were available. In addition, installation of the “smart” sharpening script was easier without the prerequisite of the Perl interpreter. Although the syntax of Scheme was challenging at first, programming in Script-Fu did not require knowledge of all of Scheme’s functions. The Procedure Browser in the Gimp aided development by listing every function with a brief description and the required parameters. Besides the Gimp, the only other program required for creating the script was a basic text editor. This project used Notepad++ Portable in Windows and the KDE Advanced Text Editor (Kate) in Linux to write the script because of their syntaxhighlighting support. However, no specific text editor was required to program in ScriptFu, provided that it could save with the .scm extension. While coding, it was helpful to keep the Gimp open to debug each line and ensure that it preformed its intended function. For testing and installation, the script was saved as smart-sharpen.scm in the Gimp’s scripts folder. The location of this scripts folder varied but could always be found by going to File>Preferences>Folders>Scripts in the Gimp. Some peculiarities of the Gimp’s Script-Fu language made coding more difficult. Gimp plug-ins written in C and the Procedure Browser listed functions with underscores, but Script-Fu required them to have dashes instead. Gimp functions always returned values as single-item lists, so the car function was required when setting variables [4]. Additionally, using Scheme’s prefix notation inside of nested parenthesis [5] caused problems when commands were coded as func(arg1) instead of (func arg1). Several parameters [6] were added to the script-fu-register procedure to create user interactivity with the script. In addition, a list after each variable’s description set the default values and checked the validity of parameters passed to the script [7]. The script’s final source code is as follows: (define (script-fu-smart-sharpen img1 draw1 edge_amt gmin1 gmax1 blur_amt gmin2 gmax2 umask_radius umask_amt umask_threshold ) ;Start script with define and enclose everything as local variable (gimp-image-undo-group-start img1) (let* ( ;Duplicate img1 to img2 (img2 (car (gimp-image-duplicate img1))) ;;(display2 (car (gimp-display-new img2))) ;Get var name of layer on img2, for later usage in edge detection (draw2 (car (gimp-image-get-active-drawable img2))) ;Decompose original image to value channel of HSV (img3 (car (plug-in-decompose 1 img1 draw1 "Value" 1))) ;;(display3 (car (gimp-display-new img3))) ;;(OLD)Create new layer called sharpening, replaced with layer-copy ;;(sharpening (car (gimp-layer-new img1 (car (gimp-image-width img1)) (car (gimp-image-height img1)) 0 "sharpening" 100 0))) ;Duplicate original layer to "sharpening" layer (sharpening (car (gimp-layer-copy draw1 0))) ) ;Now all var declarations must be called with (set! varname value) (gimp-image-add-layer img1 sharpening 0) (gimp-drawable-set-name sharpening "Sharpening") ;Add alpha channel to Sharpening layer for mask later (gimp-layer-add-alpha sharpening) ;Copy and anchor Value image to Sharpening Layer (gimp-image-set-active-layer img1 sharpening) (gimp-selection-all img3) (gimp-edit-copy-visible img3) (set! value_float (car (gimp-edit-paste sharpening 0))) (gimp-floating-sel-anchor value_float) ;Clear the selected area on the Values image (gimp-selection-none img3) ;Set Sharpening blending mode to "Value" (gimp-layer-set-mode sharpening 14) ;Now to create the edge mask :) ;Edge Detection and conversion to grayscale (plug-in-edge 1 img2 draw2 edge_amt 1 0) (gimp-image-convert-grayscale img2) ;Adjust mask by filtering insignificant edges ;Change black/white points, apply gaussian blur, and redo change levels (gimp-levels draw2 0 gmin1 gmax1 1 0 255) (plug-in-gauss 1 img2 draw2 blur_amt blur_amt 0) (gimp-levels draw2 0 gmin2 gmax2 1 0 255) ;Add white mask to Sharpening layer (set! sh_mask (car (gimp-layer-create-mask sharpening 0))) (gimp-layer-add-mask sharpening sh_mask) ;Add and anchor edge mask image to Sharpening layer ;Yay for reusable code! (gimp-selection-all img2) (gimp-edit-copy-visible img2) (set! value_float (car (gimp-edit-paste sh_mask 0))) (gimp-floating-sel-anchor value_float) (gimp-selection-none img2) ;Apply final unsharp mask ;;(gimp-image-set-active-channel img1 sh_mask) (plug-in-unsharp-mask 1 img1 sharpening umask_radius umask_amt umask_threshold) ;Remove file close confirmation for temp images, refresh displays, complete undo group (gimp-image-clean-all img2) (gimp-image-clean-all img3) (gimp-displays-flush) ) (gimp-image-undo-group-end img1) ) ;Variable Parenthesis after name: ;(default, min, max, small_inc, big_inc, precision, slider(0)/entry(1)) (script-fu-register "script-fu-smart-sharpen" _"Smart _Sharpening..." "Sharpens an image without increasing film grain.\nBased on the tutorial at\nhttp://gimpguru.org/Tutorials/SmartSharpening2/" "Benjamin Bucior " "Benjamin Bucior" "2007-07-19" "RGB*" SF-IMAGE "Image" 0 SF-DRAWABLE "Drawable" 0 SF-ADJUSTMENT _"Amount of Edge Detection" '(6 1 10 .1 1 1 0) SF-ADJUSTMENT _"Mask's Grayscale Min Before Blur" '(20 1 100 1 10 0 0) SF-ADJUSTMENT _"Mask's Grayscale Max Before Blur" '(235 155 255 1 10 0 0) SF-ADJUSTMENT _"Mask's Gaussian Blur Amount" '(5 1 15 1 5 0 0) SF-ADJUSTMENT _"Mask's Grayscale Min After Blur" '(10 1 100 1 10 0 0) SF-ADJUSTMENT _"Mask's Grayscale Max After Blur" '(235 155 255 1 10 0 0) SF-ADJUSTMENT _"Unsharp Mask Radius" '(1 .1 120 .1 1 1 0) SF-ADJUSTMENT _"Unsharp Mask Amount" '(2 0 5 .01 .1 2 0) SF-ADJUSTMENT _"Unsharp Mask Threshold" '(0 0 255 1 10 0 0) ) (script-fu-menu-register "script-fu-smart-sharpen" "/ScriptFu/Alchemy") The effectiveness of the technique was tested by comparing the results of the filters on the same image. The original testing image was an unmodified photo from a digital camera. Convolution filtering was tested by using Filters>Generic>Convolution Matrix with a standard matrix [Figure 1]; red, green, and blue channels enabled; and an extended border [8]. Unsharp masking was evaluated by using Filters>Enhance>Unsharp Mask with the default radius of 5.0, amount of 0.5, and threshold of 1. “Smart” sharpening was given the default parameters in the script to maintain consistency among results. [Figure 1: Tested convolution matrix] Results Zoomed sections of each enhanced image were analyzed to determine the effectiveness of each filter. The original image [Figure 2] without enhancement was more blurred than the others and had the softest colors. The convolution matrix filtering [Figure 3] sharpened the photo too broadly, causing noise in the picture to become worse. The unsharp mask [Figure 4] enhanced edges in the image without modifying the color of the image’s objects. “Smart” sharpening [Figure 5] enhanced the photo while increasing contrast between colors, but many soft edges became more jagged. The “smart” sharpening script greatly decreased processing time on the images. Instead of taking over 20 minutes to manually complete the tutorial, the script sharpened the image in less than