The major task in photography is motion blur.Taking clear photos under dim light using a hand-held camera is quite challenging. If the camera is set to a long exposure time, the image gets blurred due to camera shake. On the other hand, the image is dark and noisy if it is taken with a short exposure time but with a high camera gain. By combining information extracted from both blurred and nois...

Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most existing blind SR techniques use degradation estimator network explicitly estimate blur kernel guide supervision ground truth (GT) kernels. To solve this issue, it necessary design an implicit that can extract discriminati...

In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. ...

Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using information inter-frame intra-frame images. Inaccurate blur kernel estimation will enlarge distortion estimated high-resolution image. Therefore, multi-frame blind super with unknown is more challenging. For purpose reducing impact inaccurate motion on super-resolved ...

In this paper, we address the problem of jointly estimating the latent image and the depth/blur map from a single space-variantly blurred image using dictionary learning. The approach taken is based on the central idea of dictionary replacement viz. the sparse representation of a blurred image over a blurred dictionary is equivalent to that over a clean dictionary. While most of the dictionary-...

In this paper, we address the problem of jointly estimating the latent image and the depth/blur map from a single space-variantly blurred image using dictionary replacement. While most of the dictionary-based deblurring methods consider planar scenes with spaceinvariant blur, we handle 3D scenes with spacevariant blur caused by either camera motion or optical defocus. For a given blurred image,...

We present a novel stereo imaging technique called dualfocus stereo imaging or DFSI. DFSI uses a pair of images captured from different viewpoints and at different foci, but with identical wide aperture size. Each image in an DFSI pair exhibits different defocus blur, and the two images form a defocused stereo pair. To model defocus blur, we introduce a defocus kernel map (DKM) that computes th...