Asymmetric CNN for Image Superresolution

Preconditioners for regularized image superresolution

Superresolution reconstruction produces a high resolution image from a set of low resolution images. Previous work on superresolution [3, 6, 10, 12] had not adequately addressed the computational issues for this problem. In this paper, we propose efficient block circulant preconditioners for solving the regularized superresolution problem by CG. Effectiveness of our preconditioners is demonstra...

Graphcut Texture Synthesis for Single-Image Superresolution

Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture synthesis methods for texture superresolution. The Single-image Superresolution Problem The problem we are trying to solve is the following: Given: a low-resolution image and hig...

Image Compression by Cnn

A Modified Grasshopper Optimization Algorithm Combined with CNN for Content Based Image Retrieval

Nowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, t...

CNN Based Hashing for Image Retrieval

Along with data on the web increasing dramatically, hashing is becoming more and more popular as a method of approximate nearest neighbor search. Previous supervised hashing methods utilized similarity/dissimilarity matrix to get semantic information. But the matrix is not easy to construct for a new dataset. Rather than to reconstruct the matrix, we proposed a straightforward CNN-based hashing...