nils-herrmann/clean_scirepeval huggingface dataset:
Single image haze removal via a simplified dark channel

Images of outdoor scenes could be degraded by haze, fog, and smoke in the atmosphere. In this paper, we propose a novel single image haze removal algorithm by introducing a minimal color channel and a sky region compensation term. A simplified dark channel is computed via the minimal color channel. The transmission map is first estimated by using the simplified dark channel. To avoid amplifying noise in the sky, a non-negative sky region compensation term is proposed to adjust the transmission map in the sky. The map is then refined via a content adaptive guided image filter and is finally applied to recover the haze image. Experimental results on outdoor images with haze and without haze demonstrate that the proposed algorithm outperforms existing algorithms.
Superpixel-Based Single Nighttime Image Haze Removal

Haze removal is important to improve performance of outdoor vision systems. However, it is challenging to remove haze from a single nighttime haze image. In this paper, a novel superpixel-based single image haze removal algorithm is proposed for nighttime haze images. The input nighttime image is first decomposed into a glow image and a glow-free nighttime haze image using their relative smoothness. A superpixel-based method is then introduced to compute the value of the atmospheric light and dark channel for each pixel in the glow-free haze image. The transmission map is decomposed from the dark channel of the glow-free haze image by the weighted guided image filter. Since superpixels usually adhere to the boundaries of objects well, a smaller local window size can be selected. As such, details in areas of fine structures are preserved better. In addition, to avoid noticeable noise in the sky area, an adaptive threshold is added to the transmission map when the nighttime haze image is restored. Experiments show that our method produces better results than the existing haze removal algorithms for nighttime haze images.
Robust generation of high dynamic range images

A robust scheme is proposed to generate an anti-ghosting high dynamic range (HDR) image from a set of low dynamic range (LDR) images with different exposure times. Three major contributions of this paper are 1) a bi-directional prediction method; 2) an adaptive threshold for the classification of pixels; 3) Bayes estimator based methods for the on-line updating of predicted values and the synthesis of pixels to fill in the regions of moving objects to preserve their dynamic ranges. The proposed scheme is suitable for both static and dynamic scenes.
Mode-Dependent Transforms for Coding Directional Intra Prediction Residuals

The use of mode-dependent transforms for coding directional intra prediction residuals has been previously shown to provide coding gains, but the transform matrices have to be derived from training. In this paper, we derive a set of separable mode-dependent transforms by using a simple separable, directional, and anisotropic image correlation model. Our analysis shows that only one additional transform, the odd type-3 discrete sine transform (ODST-3), is required for the optimal implementation of mode-dependent transforms. In addition, the four-point ODST-3 also has a structure that can be exploited to reduce the operation count of the transform operation. Experimental results show that in terms of coding efficiency, our proposed approach matches or improves upon the performance of a mode-dependent transforms approach that uses transform matrices obtained through training.
An unequal packet loss resilience scheme for video over the Internet

We present an unequal packet loss resilience scheme for robust transmission of video over the Internet. By jointly exploiting the unequal importance existing in different levels of syntax hierarchy in video coding schemes, GOP-level and Resynchronization-packet-level Integrated Protection (GRIP) is designed for joint unequal loss protection (ULP) in these two levels using forward error correction (FEC) across packets. Two algorithms are developed to achieve efficient FEC assignment for the proposed GRIP framework: a model-based FEC assignment algorithm and a heuristic FEC assignment algorithm. The model-based FEC assignment algorithm is to achieve optimal allocation of FEC codes based on a simple but effective performance metric, namely distortion-weighted expected length of error propagation, which is adopted to quantify the temporal propagation effect of packet loss on video quality degradation. The heuristic FEC assignment algorithm aims at providing a much simpler yet effective FEC assignment with little computational complexity. The proposed GRIP together with any of the two developed FEC assignment algorithms demonstrates strong robustness against burst packet losses with adaptation to different channel status.
Pricing based differentiated bandwidth service

In this paper, we design a framework that is composed of feedback signals and the corresponding source adaptation scheme to provide differentiated bandwidth service for elastic and inelastic applications. The feedback signals consist of a controlling signal and a charging signal. The continuous control signal of the most congested link is used as the control signal for each source to adjust the sending rate. The sum of link prices will serve as the charging signal to determine the cost that each source should pay and the distribution of the profit among service providers. Traffic can achieve weighted Max-Min fairness by adjusting its sending rate according to the feedback signals, the corresponding source adaptation scheme and its willingness-to-pay. Our pricing system allows us to formulate a reasonable pricing framework to charge the sources. With the proposed scheme, each link has the incentive to increase its capacity because it can get more profit. This is very helpful for the evolution of the Internet.
Unequal packet loss resilience for MPEG-4 video over the Internet

In this paper, an unequal packet loss resilience scheme is proposed for robust transmission of MPEG-4 video over the Internet. By exploiting the unequal importance regularly distributed in different levels of MPEG-4 video coding hierarchy, a GOP- and resynchronization-packet-level integrated protection scheme (GRIP) is designed to integrate unequal loss protection (ULP) for these two levels together using forward error protection (FEC) across packets. Experimental results show that the proposed GRIP scheme has good performance against burst packet losses and can adapt to time-varying channel status fairly well.
Simplified Motion-Refined Scheme for Fine-Granularity Scalability

In this paper, we introduce a low-complexity fine-granularity scalable (FGS) video encoder that refines both residue and motion information in the quality layers. The current scalable video coding (SVC) draft shows that significant gains can be achieved when each enhancement layer undergoes the motion estimation/motion compensation (ME/MC) process with its own motion vector field (MVF). However, given the high computational cost of ME/MC, a motion-refined FGS scheme can be expensive to implement. The proposed scheme controls the macroblock (MB) mode allowed in the base layer and channels computational resources to refine motion in enhancement layers. Through a proper selection of Lagrangian factor for the generation of the first MVF, it is possible to design a low-complexity FGS encoder that has good overall coding performance. A simplified motion-refinement scheme is also adopted for selected MBs in enhancement layers by exploiting the correlation of MB-type information between successive layers to further reduce the complexity of the FGS encoder. Meanwhile, a framework of rate-distortion-complexity optimization is proposed for the FGS by considering the interpolation complexity during the ME/MC in each layer. The FGS decoder can be simplified through the reduction in the number of interpolations.
Single Image De-Hazing Using Globally Guided Image Filtering.

Local edge-preserving smoothing techniques such as guided image filtering (GIF) and weighted guided image filtering (WGIF) could not preserve fine structure. In this paper, a new globally guided image filtering (G-GIF) is introduced to overcome the problem. The G-GIF is composed of a global structure transfer filter and a global edge-preserving smoothing filter. The proposed filter is applied to study single image haze removal. Experimental results show that fine structure of the dehazed image is indeed preserved better by the proposed G-GIF and the dehazed images by the proposed G-GIF are sharper than those dehazed images by the existing GIF.
Intelligent detail enhancement for differently exposed images

Multi-scale exposure fusion is a fast approach to fuse several differently exposed images captured at the same high dynamic range (HDR) scene into a high quality low dynamic range (LDR) image. The fused image is expected to include all details of the input images, however, the details in the brightest and darkest regions are usually not preserved well. Adding details that are extracted from the input images to the fused image is an efficient approach to overcome the problem. In this paper, a fast selectively detail enhancement algorithm is proposed to extract the details in the brightest and darkest regions of the HDR scene and add the extracted details to the fused image. Experimental results show that the proposed algorithm can enhance the details of the fused image much faster than the existing algorithms with comparable or even better visual quality.
Geometrically determining leaky bucket parameters for video streaming over constant bit-rate channels

For streaming of pre-encoded bitstreams over constant bit-rate (CBR) channels, the channel bandwidth, the receiver buffer capacity as well as the latency requirement vary greatly from application to application. We propose an algorithm to determine the minimum buffer size and the minimum start-up delay required for streaming a pre-encoded bitstream over CBR channels at any specific bit rate. The proposed method employs geometric operations to derive the optimal determination for low or high bit rates and sub-optimal determination for medium bit rates. The results have been compared with the H.264/AVC hypothetical reference decoder. The proposed approach provides a theoretical insight and a simple but effective algorithm for determining the leaky bucket parameters for video streaming over CBR channels.
Fast mode decision for spatial scalable video coding

Scalable video coding (SVC) is an on-going standard and the current working draft (WD) is an extension of H.264/AVC. It provides scalability at the bit stream level with good compression efficiency, and allowing free combinations of spatial, temporal and SNR scalability. In the WD, exhaustive search technique is employed to select the best coding mode for each macroblock. This technique achieves highest possible coding efficiency, but it results in higher computational complexity. To overcome this, we propose a novel fast mode decision scheme for spatial scalability in SVC. In this scheme, the mode distribution relationship between base layer and enhancement layers is employed to reduce the candidate mode set at enhancement layers. The experimental results show that the proposed scheme provides significant reduction in computational complexity without any noticeable coding loss
Recovering real-world scene: high-quality image inpainting using multi-exposed references

A novel method of high-quality image inpainting for recovering an original scene from degraded images using reference images of different exposures is proposed. It consists of a new inter-pixel relationship function and the respective refinement to synthesise missing pixels from existing spatially co-related pixels, and a dual patching to minimise the noise caused by dynamic range lost. Experiments on the method have been conducted and the results demonstrate the reliability of the proposed method.
Rate Control of H.264/AVC Scalable Extension

This paper presents a rate control scheme for H.264/AVC scalable extension. Based on our previous work on H.264/AVC rate control, a switched model is proposed to predict the mean absolute difference (MAD) of the residual texture from the available MAD information of the previous frame in the same layer and the same frame in its ldquobase layer.rdquo Thus, abrupt MAD fluctuations could be predicted properly in the enhancement layer. Moreover, a bit allocation scheme is proposed for the hierarchical B-frames structure by taking into consideration the relative importance of each frame. With our algorithm, the rate control for all the coarse-grain-scalability, spatial, temporal and combined enhancement layer could be realized, and the target bit rate for each layer can be achieved. Our method encodes the sequence only once and the buffer is well controlled to prevent it from overflowing and under flowing.
Low-complexity mode-dependent KLT for block-based intra coding

Applying mode-dependent separable transforms, e.g., mode-dependent directional transform (MDDT), is an effective method for improving transform coding of intra prediction residuals. However, two transform matrices typically need to be stored for each intra prediction mode. By using a simple image correlation mode, we have previously derived and proposed a simplified mode-dependent separable transforms scheme that uses a combination of two well-known transforms: Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST). In this paper, we propose an orthogonal 4-point integer DST that has a multiplier-less implementation consisting of only adds and bit-shifts. We also propose a simple set of mode-dependent scans for coefficient coding that can be used on top of mode-dependent transforms. Our experimental results on the current HEVC reference software show that in terms of coding efficiency, our proposed approach has comparable performance to MDDT. More importantly, compared to MDDT, our approach requires no training and has lower computational and storage costs.
Wyner-Ziv Image Coding from Random Projections

In this paper, we present a Wyner-Ziv coding based on random projections for image compression with side information at the decoder. The proposed coder consists of random projections (RPs), nested scalar quantization (NSQ), and Slepian-Wolf coding (SWC). Most of natural images are compressible or sparse in the sense that they are well-approximated by a linear combination of a few coefficients taken from a known basis, e.g., FFT or Wavelet basis. Recent results show that it is surprisingly possible to reconstruct compressible signal to within very high accuracy from limited random projections by solving a simple convex optimization program. Nested quantization provides a practical scheme for lossy source coding with side information at the decoder to achieve further compression. SWC is lossless source coding with side information at the decoder. In this paper, ideal SWC is assumed, thus rates are conditional entropies of NSQ quantization indices. Recently theoretical analysis shows that for the quadratic Gaussian case and at high rate, NSQ with ideal SWC performs the same as conventional entropy-coded quantization with side information available at both the encoder and decoder. We note that the measurements of random projects for a natural large-size image can behave like Gaussian random variables because most of random measurement matrices behave like Gaussian ones if their sizes are large. Hence, by combining random projections with NSQ and SWC, the tradeoff between compression rate and distortion will be improved. Simulation results support the proposed joint codec design and demonstrate considerable performance of the proposed compression systems.
Fast localized single cell membrane poration by bubble-induced jetting flow

This paper demonstrates a new method to porate single suspension living cell membrane using cavitation bubble-induced high speed jet flow. A microfluidic chip with an array of single cell trapping structures is designed and fabricated to trap sing cell and induce the asymmetric collapse of the cavitation bubble. Myeloma cells suspended in Trypan blue saline solution is tested and single Myeloma cells can be trapped in the trapping structures. The dynamic process is recorded using a high speed camera. This method has great potential in biomedical applications and easy to be integrated to other microfluidic system.
Selectively Detail-Enhanced Fusion of Differently Exposed Images With Moving Objects

In this paper, we introduce an exposure fusion scheme for differently exposed images with moving objects. The proposed scheme comprises a ghost removal algorithm in a low dynamic range domain and a selectively detail-enhanced exposure fusion algorithm. The proposed ghost removal algorithm includes a bidirectional normalization-based method for the detection of nonconsistent pixels and a two-round hybrid method for the correction of nonconsistent pixels. Our detail-enhanced exposure fusion algorithm includes a content adaptive bilateral filter, which extracts fine details from all the corrected images simultaneously in gradient domain. The final image is synthesized by selectively adding the extracted fine details to an intermediate image that is generated by fusing all the corrected images via an existing multiscale algorithm. The proposed exposure fusion algorithm allows fine details to be exaggerated while existing exposure fusion algorithms do not provide such an option. The proposed scheme usually outperforms existing exposure fusion schemes when there are moving objects in real scenes. In addition, the proposed ghost removal algorithm is simpler than existing ghost removal algorithms and is suitable for mobile devices with limited computational resource.
Synchronization of Chaotic Systems in the Presence of Noise

Recently, a lot of efforts have been devoted to the synchronization of chaotic systems for chaotic secure communication. However, all the results on perfect synchronization are obtained under the ideal condition that the chaotic systems in both the transmitter side and the receiver side are noise free. In this paper, we relax this condition by designing a controller for the driven system based on the bounds of the system noise. Perfect synchronization of the two systems is theoretically established. Numerical results also illustrate the robustness and effectiveness of the proposed scheme
Intelligent Detail Enhancement for Exposure Fusion

Multiscale exposure fusion is a fast approach to fuse several differently exposed images captured at the same high dynamic range (HDR) scene into a high-quality low-dynamic range (LDR) image. The fused image is expected to include all details of the input images. However the details in the brightest and darkest regions are usually not well preserved. Adding details that are extracted from the input images to the fused image is an efficient approach to overcome the problem. In this paper a new gradient domain weighted least square based image smoothing algorithm is proposed to extract the details in the brightest and darkest regions of the HDR scene. The extracted details are then added to an image that is produced using an edge-preserving smoothing pyramid based multiscale exposure fusion algorithm. Experimental results show that the proposed detail enhanced exposure fusion algorithm can preserve details in saturated regions especially the brightest regions better than the state-of-the-art multiscale exposure fusion algorithms.
Unequal error protection for motion compensated video streaming over the Internet

This paper presents an unequal error protection scheme for motion compensated video over the Internet. The forward error correction (FEC) codes are optimally assigned to different frames in group of picture (GOP) by exploiting the temporal dependency among frames. To achieve optimal allocation of FEC assigned in GOP, we propose a performance criterion for the measurement of allocation, namely the expected length of error propagation (ELEP), which makes sense intuitively, as fewer frames corrupted implies better quality of reconstruction. Experiment results show the proposed scheme is robust to burst packet loss in the Internet. More importantly, graceful degradation of video quality is achieved by the proposed scheme as the packet loss probability of an Internet connection increases.
Super-pixel based single image haze removal

Single image dehazing is challenging because the number of freedoms is larger than the number of observations. Haze removal based on dark channel prior is effective, while it is an issue to determine the value of dark channel for each pixel. In this paper, a novel super-pixel based method is introduced to compute the value of dark channel for each pixel. A popular superpixel segmentation algorithm, simple linear iterative clustering (SLIC) is adopted to decompose an input haze image into super-pixels. After finding each super-pixel's dark channel, the dark channels for all the pixels in the super-pixel are set as the dark channel of the super-pixel. The transmission map is estimated from the dark channel and refined by the Guided Filter. In addition, an adaptive threshold is introduced for the transmission map to avoid amplifying noise in the sky region. Experiments show that our method has a good result with low computation.
Intensity mapping function based weighted frame averaging for high dynamic range imaging

A high quality image can be synthesized by combining several differently exposed low dynamic range (LDR) images of the same scene. For scenes under low lighting condition, cameras are usually set to high sensitivity mode to reduce exposure times and avoid motion blur on captured images. However, those images tend to be noisy and the noise severely degrades the visual quality of final image especially on dark areas. In this paper, a weighted frame averaging method based on an intensity mapping function (IMF) is proposed to reduce noise from the input LDR images with shorter exposures. The proposed method does not require any knowledge on either camera response functions (CRFs) or exposure times, and it is much simpler than the state-of-art method. Experiment results also show that the proposed method effectively removes the noise from the shortly exposed LDR images without introducing any blurring or other artifacts.
High dynamic range compression by half quadratic regularization

This paper presents a new adaptive tone mapping for high dynamic range (HDR) images. In the proposed scheme, the luminance of an HDR image is decomposed into a base layer with large gradients and a detail layer with small gradients by using an adaptive half quadratic regularization method. The base layer is compressed by a novel global mapping to reduce the dynamic range while the detail layer can be amplified to enhance the local contrasts. With the proposed scheme, the generated low dynamic range (LDR) images look more natural and local details are also preserved very well.