News

Single image super-resolution (SISR) is an intensively studied topic in image processing. It aims at recovering a high-resolution (HR) image from its low- resolution (LR) counterpart. SISR finds wide real-world applications such as remote sensing, medical imaging, and biometric identification. Besides, it attracts attention due to its connection with other tasks (e.g., image registration, compression, and synthesis).To deal with such ill-posed problem, we recently proposed two methods, LSR[1] and LSR++[2], by providing reasonable performance and effectively reduced complexity. LSR consists of three cascaded modules:
1) Unsupervised Representation Learning by creating a pool of rich and diversified representations in the neighborhood of a target pixel,
2) Supervised Feature Learning by Relative Feature Test (RFT [3]) to select a subset from the representation pool that is most relevant to the underlying super-resolution task automatically, and
3) Supervised Decision Learning by predicting the residual of the target pixel based on the selected features through regression via classical machine learning, and effectively fusioning the predictions for more stable results. LSR++ is promoted based on LSR, with emphasis on sample alignment, a more promising sample preparation process which is suitable for all patch-based computer vision problems. As illustrated in Fig 1, based on gradient histograms of patches along the eight reference directions (Fig.1.a), patch alignment utilizes patch rotations and flipping to meet the standard templates of gradient histograms, where D_max is the direction with the largest cumulative gradient magnitude, and D_max_orth_b and D_max_orth_s refer to the orthogonal directions to D_max with big and small cumulative gradient magnitude, respectively. By modifying the set of (D_max, D_max_orth_b, and D_max_orth_s) of a patch, patch alignment can regularize the edge pattern with the patch by directions perpendicular the edge (D_max) and directions along [...]

With the rapid development of point cloud applications, we have witnessed the prosperity of point cloud coding techniques in recent years. These point cloud codecs yield various compression artifacts, posing challenges to the point cloud quality assessment. Current PCQA metrics cannot handle the complicated compression distortion effectively. To overcome the challenge, we attend the ICIP 2023 point cloud visual quality assessment (PCVQA) grand challenge[1], and our BPQA model[2] achieved a competitive result over the BASICS[3] dataset.

Our proposed BPQA model consists of three modules. First, it selects points of various salience degrees based on the color information. Second, it projects the local neighborhood of selected points along one of the three orthogonal axes to yield a five-channel map (namely, RGB, depth, and pairwise-point-distance-mean channels). Third, it extracts features using the channel-wise Saab transform (c/w Saab) and the relevant feature test (RFT) and trains an XGBoost regressor to predict the Mean Opinion Score (MOS). BPQA offers competitive performance in no-reference quality assessment tasks of the ICIP 2023 PCVQA Challenge.

Reference
[1]https://sites.google.com/view/icip2023-pcvqa-grand-challenge/
[2]Q. Zhou, A. Feng, T.-S. Yang, S. Liu, and C.-C. J. Kuo, “Bpqa: A blind point cloud quality assessment method,” in 2023 IEEE International Conference on image processing (ICIP). IEEE, 2023.
[3] A. Ak, E. Zerman, M. Quach, A. Chetouani, A. Smolic, G. Valenzise, and P. L. Callet, “Basics: Broad quality assessment of static point clouds in compression scenarios,” ArXiv, vol. abs/2302.04796, 2023

-By Qingyang Zhou

Congratulations to Zohreh Azizi for passing her defense on Aug 29th. Zohreh’s thesis is titled “Advanced Technologies for Learning-based Image/Video Enhancement, Image Generation and Attribute Editing.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). We thank Zohreh for her kind sharing and wish her all the best in the next journey.

“My thesis proposes novel methodologies in four main areas related to visual data:

1. Low-light image enhancement: We present a new method, called NATLE, which attempts to strike a balance between noise removal and natural texture preservation through a low-complexity solution.

2. Low-light video enhancement: We also present a self-supervised adaptive low-light video enhancement method, called SALVE. The combination of traditional retinex-based image enhancement and learning-based ridge regression in SALVE leads to a robust, adaptive and computationally inexpensive solution. Our user study shows that 87% of participants prefer SALVE over prior work.

3. Image generation: Then, we present a generative modeling approach based on successive subspace learning (SSL). The resulting method, called the progressive attribute-guided extendable robust image generative (PAGER) model, has advantages in mathematical transparency, progressive content generation, lower training time, robust performance with fewer training samples, and extendibility to conditional image generation.

4. Facial Attribute Editing: Finally, we present a facial attribute editing method based on Gaussian Mixture Model (GMM). Our proposed method, named AttGMM, has a great advantage in lowering the computational cost.

t’s hard to believe my four-year PhD journey at MCL has reached its end. When you are inside the process, sometimes it’s not easy to keep up your hope. But each time you decide not to give up, you pave your path one more step closer to success.

I have grown into a much more confident person thorough [...]

USC Fall 2023 starts on the coming Monday, August 21, and MCL will resume the lab weekly activities on campus at the same time. Hope everyone have a great Fall semester!

Image credit:

Campus Benefits

We are so happy to welcome a new graduate member, Jiawei Yu, joining MCL this summer. Here is a quick interview with Jiawei:

1. Could you briefly introduce yourself and your research interests?

My name is Jiawei Yu. Currently, I am pursuing my Master’s degree of Computer Science at University of Southern California. I joined MCL as a summer intern this year, and my research interests are related to machine learning and computer vision. For my hobbies, I like playing the piano during my free time. Besides, I like skiing very much.

2. What is your impression about MCL and USC?

MCL is a well-organized lab, and each MCL member is intelligent, creative, hardworking, friendly, and helpful. Besides, I really enjoy working in MCL because there is a good academic atmosphere. I really appreciate the suggestions and guidance provided by Prof. Kuo, and they helped me learn and grow a lot. For USC, the campus is very beautiful. Moreover, USC enjoys a high reputation and has outstanding academic achievements.

3. What is your future expectation and plan in MCL?

Currently, I am working with Prof. Kuo and Qingyang on the project related to point cloud surface reconstruction. This project is interesting and valuable, and I have learned lots of useful knowledge during the process. Besides, I think that MCL is very excellent and promising, and I hope that MCL will continue to thrive and have more outstanding academic achievements in the future.