Low-resolution Color Image Research Articles

Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.

Low-Light Color Imaging via Cross-Camera Synthesis

This paper presents a framework for low-light color imaging using a dual camera system that combines a high spatial resolution monochromatic (HSR-mono) image and a low spatial resolution color (LSR-color) image. We propose a cross-camera synthesis (CCS) module to learn and transfer illumination, color, and resolution attributes across paired HSR-mono and LSR-color images to recover brightness- and color-adjusted high spatial resolution color (HSR-color) images at both camera views. Jointly characterizing various attributes for final synthesis is extremely challenging because of significant domain gaps across cameras. Our proposed CCS method consists of three subtasks: reference-based illumination enhancement ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefIE</b> ), reference-based appearance transfer ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefAT</b> ), and reference-based super resolution ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefSR</b> ), by which we can characterize, transfer, and enhance illumination, color, and resolution at both views. Each subtask is implemented using deep neural networks (DNNs) that are first trained for each subtask separately and then finetuned jointly. Experimental results show the superior qualitative and quantitative results of the proposed CCS model on both synthetic content from popular datasets and real-captured scenes. Ablation studies further verify the model generalization to various exposures and camera baselines. We will make our work accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://njuvision.github.io/CCS</uri> for reproducible research.

Single color image super-resolution using sparse representation and color constraint

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L 1 or L 2 ). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L 2 / 3 sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L 2 / 3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

Multi-Frame RGB/NIR Imaging for Low-Light Color Image Super-Resolution

We propose a method for super-resolution (SR) of low-resolution (LR) color images taken in low-light scenes. Our method is based on the multi-frame SR technique, which reconstructs a high-resolution color image by fusing multiple LR images that were taken at different camera positions so as to alter the alignment between scene details and pixels. Previous multi-frame SR methods have implicitly assumed that the LR images could be captured with less noise and blur. However, images taken in low-light scene will contain high level of noise and motion blur, making it difficult to achieve high quality SR. To overcome these problems, we utilize a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) information. We capture multiple raw images of a low-light scene using our RGB/NIR single sensor with an NIR flash unit. Because a NIR flash can provide sufficient illumination for low-light scenes, the structural scene information that, contributes to an effective SR process, can be captured with high quality. With the help of the NIR information, we jointly perform deblurring, denoising and SR of LR RGB/NIR raw images. Our experiments demonstrate the effectiveness of the proposed method using synthetic and real RGB/NIR raw images.

Image restoration and color fusion of digital microscopes.

Traditional microscopes do not meet a wide field of view and high resolution at the same time. We propose a method for image restoration and color fusion of digital microscopes. It combines a single high-resolution gray scale hologram with a low-resolution color image to obtain a high-resolution color image. Specifically, the high-resolution gray scale image is obtained by reconstructing three different height holograms using a wavelet-based method, and the color information is obtained using a portable cell phone microscope. The subsequent calibration and blending of colors ultimately results in a high-resolution, wide-field color map that can be of great help in the study of pathology or biomedicine. This method breaks the rule that the large field of view and high resolution of a traditional microscope cannot be simultaneously satisfied, which can realize a more comprehensive observation of the shape and details of the slice.

Simultaneous color-depth super-resolution with conditional generative adversarial networks

In this paper, color-depth conditional generative adversarial networks (CDcGAN) are proposed to resolve the problems of simultaneous color image super-resolution and depth image super-resolution in 3D videos. Firstly, a generative network is presented to leverage the mutual information of the low-resolution color image and low-resolution depth image so that they can enhance each other considering their geometric structural similarity in the same scene. Secondly, three auxiliary losses of data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network to ensure that the generated images are close to the real ones in addition to the adversarial loss. Finally, we study the CDcGAN and its variants. Experimental results show that the proposed approach can produce the high-quality color image and depth image from a pair of low-quality images, and it is superior to several other leading methods. Additionally, it has also been used to resolve the problems of concurrent image smoothing and edge detection, as well as the problem of HR-color-image-guided depth super-resolution to show the effectiveness and universality of the proposed method.

Mixed-primary factorization for dual-frame computational displays

Increasing resolution and dynamic range of digital color displays is challenging with designs confined by cost and power specifications. This necessitates modern displays to trade-off spatial and temporal resolution for color reproduction capability. In this work we explore the idea of joint hardware and algorithm design to balance such trade-offs. We introduce a system that uses content-adaptive and compressive factorizations to reproduce colors. Each target frame is factorized into two products of high-resolution monochromatic and low-resolution color images, which then get integrated through temporal or spatial multiplexing. As our framework minimizes the error in colorimetric space, the perceived color rendition is high, and thanks to GPU acceleration, the results are generated in real-time. We evaluate our system with a LCD prototype that uses LED backlight array and temporal multiplexing to reproduce color images. Our approach enables high effective resolution and dynamic range without increasing power consumption. We also demonstrate low-cost extensions to hyperspectral and light-field imaging, which are possible due to compressive nature of our system.

Precise localization of eye centers in low resolution color images

The localization of eye centers and tracking of gaze constitutes an integral component of many human–computer interaction applications. A number of constraints including intrusiveness, mobility, robustness and high-price of eye tracking systems have hindered the way of the integration of eye trackers in everyday applications. Several ‘passive’ systems based on a single camera have been lately proposed in the literature, exhibiting though subordinate precision compared to the commercial, hardware-based eye tracking devices. In this paper we introduce an automatic, non-intrusive method for precise eye center localization in low resolution images, acquired from single low-cost cameras. To this end, the proposed system uses color information to derive a novel eye map that emphasizes the iris area and a radial symmetry transform which operates both on the original eye images and the eye map. The performance of the proposed method is extensively evaluated on four publicly available databases containing low resolution images and videos. Experimental results demonstrate great accuracy in challenging cases and resilience to pose and illumination variations, achieving significant improvement over existing methods.