Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Band-Sifting Decomposition for Image-Based Material Editing

Photographers often "prep" their subjects to achieve various effects; for example, toning down overly shiny skin, covering blotches, etc. Making such adjustments digitally after a shoot is possible, but difficult without good tools and good skills. Making such adjustments to video footage is harder still. We describe and study a set of 2D image operations, based on multiscale image analysis, that are easy and straightforward and that can consistently modify perceived material properties. These operators first build a subband decomposition of the image and then selectively modify the coefficients within the subbands. We call this selection process band sifting. We show that different siftings of the coefficients can be used to modify the appearance of properties such as gloss, smoothness, pigmentation, or weathering. The band-sifting operators have particularly striking effects when applied to faces; they can provide "knobs" to make a face look wetter or drier, younger or older, and with heavy or light variation in pigmentation. Through user studies, we identify a set of operators that yield consistent subjective effects for a variety of materials and scenes. We demonstrate that these operators are also useful for processing video sequences

An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method

Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between depth and the difference between the hazy image’s maximum and lowest color channels inspired the suggested study. Using a contrasting attention mechanism spanning sub-pixels and blocks, we offer a unique attention method to create high-quality, haze-free pictures. The L*a*b* color model has been proposed as an effective color space for dehazing images. A variational auto-encoder-based dehazing network may also be utilized for training since it compresses and attempts to reconstruct input images. Estimating hundreds of image-impacting characteristics may be necessary. In a variational auto-encoder, fuzzy input images are directly given a Gaussian probability distribution, and the variational auto-encoder estimates the distribution parameters. A quantitative and qualitative study of the RESIDE dataset will show the suggested method's accuracy and resilience. RESIDE’s subsets of synthetic and real-world single-image dehazing examples are utilized for training and assessment. Enhance the structural similarity index measure (SSIM) and peak signal-to-noise ratio metrics (PSNR)