This study sought an effective detection method not only for flame but also the smoke generated in event of a fire. To this end, region was pre-processed using color conversion and corner method, could be detected dark channel prior optical flow. eliminates unnecessary background regions allows selection fire-related regions. Where there interest, inference conducted deep-learning-based convolu...

Computed tomography (CT) is used to diagnose diseases and tumors. A special dye called contrast material in CT scans assist emphasize the parts of body being examined. Therefore, an enhancement technique improve images' degradation needed. This paper aims present a method enhance quality Ct-Scan images based on discrete wavelets transform Retinex algorithm. The proposed methods are algorithm pa...

Learning a generalized prior for natural image restoration is an important yet challenging task. Early methods mostly involved handcrafted priors including normalized sparsity, $$\ell _0$$ gradients, dark channel priors, etc. Recently, deep neural networks have been used to learn various but do not guarantee generalize. In this paper, we propose novel approach that embeds task-agnostic into tra...

We study the suppression of the small-scale power spectrum due to the decay of charged matter to dark matter prior to recombination. Prior to decay, the charged particles couple to the photon-baryon fluid and participate in its acoustic oscillations. However, after these charged particles decay to neutral dark matter, the photon-baryon fluid is coupled only gravitationally to the newly created ...

Blind image deblurring (BID) is a long-standing challenging problem in low-level processing. To achieve visually pleasing results, it of utmost importance to select good priors. In this work, we develop the ratio dark channel prior (DCP) bright (BCP) as an for solving BID problem. Specifically, above two priors obtained from RGB images are used construct innovative sparse at first, and then lea...