Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Zheng, Chaobing"'
Unsupervised learning based multi-scale exposure fusion (ULMEF) is efficient for fusing differently exposed low dynamic range (LDR) images into a higher quality LDR image for a high dynamic range (HDR) scene. Unlike supervised learning, loss function
Externí odkaz:
http://arxiv.org/abs/2409.17830
Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for
Externí odkaz:
http://arxiv.org/abs/2409.04679
It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. Although the CNN based methods have reported promising performance recently, there are still some defects, such as dat
Externí odkaz:
http://arxiv.org/abs/2305.02100
It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based te
Externí odkaz:
http://arxiv.org/abs/2209.07808
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with hi
Externí odkaz:
http://arxiv.org/abs/2209.05913
Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a no
Externí odkaz:
http://arxiv.org/abs/2201.06823
Publikováno v:
2022 IEEE International Conference on Image Processing
Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper,
Externí odkaz:
http://arxiv.org/abs/2111.10943
Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is propos
Externí odkaz:
http://arxiv.org/abs/2111.05701
Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single ima
Externí odkaz:
http://arxiv.org/abs/2111.05700
There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of t
Externí odkaz:
http://arxiv.org/abs/2111.06038