CASR-Net: A color-aware super-resolution network for panchromatic image

Liu, Ling; Jiang, Qian; Jin, Xin; Feng, Jianan; Wang, Ruxin; Liao, Hangying; Lee, Shin-Jye; Yao, Shaowen

doi:10.1016/j.engappai.2022.105084

Cited by 13 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For coloring of limited data, they propose the model. Liu et al [33] proposed a superresolution network with color awareness that combines the concepts of picture colorization and superresolution to enhance panchromatic pictures' spectral and spatial resolution. Kumar et al [34] proposed a pairing model of the Siamese network and convolutional neural network for image colorization.…”

Section: Learning-based Colorizationmentioning

confidence: 99%

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Gain¹,

Rahman²,

Debnath³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel. To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter. 2 stage k-means clustering technique is applied to divide the subregions. Then we apply Gamma transformation in the entire a * b * channel to saturate the image. We compare our proposed method with several existing methods. From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize. Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.

show abstract

Section: Learning-based Colorizationmentioning

confidence: 99%

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Gain¹,

Rahman²,

Debnath³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The pixel value is related to the intensity of solar radiation reflected by the target. Panchromatic images generally have a high spatial resolution, but their images have little spectral information [94].…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

show abstract

“…al. [32] proposed a super-resolution network with color awareness that combines the concepts of picture colorization and super-resolution to enhance panchromatic pictures' spectral and spatial resolution. Kumar et.…”

Section: B Related Workmentioning

confidence: 99%

A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Gain

Debnath

2023

IEEE Access

View full text Add to dashboard Cite

Gray to Color conversion causes difficulties because of the nature of its intrinsic multimodality. Despite recent significant advancements in this domain by numerous learning-based approaches, there still have two drawbacks: i) implausible color assignment and ii) contextual ambiguity. Recently deep learning models are being used for colorization as they outperform others. In a training image, desaturated color components are greater than saturated color components due to the larger background areas (clouds, pavement, dirt, walls, etc.) compared to the focused objects. This imbalanced feature representation biases the learning model in favor of major features. However, small regions with specific colors are the region of interest. To solve this problem, we proposed the Deep Localization Network (DL-Net) by modifying the mean squared error backpropagation algorithm. We compute chromatic component-based Local Losses (LLs) which are the primary component of the proposed DL-Net. The LL employs priority on rare semantic components of the original image features. It works to improve diverse-range dependency modeling in an effort to reduce contextual ambiguity and color leakage that promotes the production of more plausible coloring. With a number of current methodologies, we contrast our proposed approach. The experimental findings demonstrate that our proposed method produces good colorization of images and outperforms other methods in terms of SSIM, MSE, and PSNR quality criteria.

show abstract

CASR-Net: A color-aware super-resolution network for panchromatic image

Cited by 13 publications

References 23 publications

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Contact Info

Product

Resources

About